arXiv:2606.10825v1 Announce Type: new Abstract: Diffusion policies (DPs) have emerged as expressive policy representations for robot learning, often used with imitation learning methods such as behavioral cloning (BC). However, while their success has largely been confined to BC, direct reinforcement learning (RL) fine-tuning remains challenging because actions are generated through a multi-step denoising process. In this work, we propose MODIP, a framework for the offline-to-online fine-tuning of DPs. Rather than directly applying RL to the DPs, MODIP leverages a world model (WM) to guide pol
The increased interest in diffusion models for robotics necessitated a more efficient and effective way to fine-tune these complex policies with reinforcement learning, addressing prior limitations in direct application.
This development could significantly improve the efficiency and applicability of diffusion policies in robotics, enabling more sophisticated and robust robotic behaviors and accelerating AI's integration into physical systems.
The previous difficulty in fine-tuning diffusion policies with reinforcement learning is mitigated by MODIP's framework, which leverages a world model to guide the optimization process.
- · Robotics companies
- · AI research institutions
- · Automation sector
- · Companies developing intelligent agents
- · Methods relying solely on behavioral cloning
- · Less efficient RL fine-tuning approaches
More capable and autonomous robots due to advanced policy learning.
Accelerated deployment of AI agents in real-world physical tasks and environments.
Enhanced development of general-purpose AI systems that can learn and adapt effectively in complex situations.
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Read at arXiv cs.LG